{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "contained-cloud",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 1.41421356, 1.73205081, 2.        ,\n",
       "       2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ,\n",
       "       3.16227766, 3.31662479, 3.46410162, 3.60555128, 3.74165739])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.arange(15)\n",
    "\n",
    "np.sqrt(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "related-investment",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True, False, False, False, False,\n",
       "       False, False, False, False, False, False])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(15)\n",
    "a[4] = 99\n",
    "b = np.arange(15)\n",
    "b[:] = 88\n",
    "np.greater(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "logical-sudan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n",
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1015"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(15)\n",
    "print(a)\n",
    "b = np.arange(15)\n",
    "print(b)\n",
    "np.multiply(a, b)\n",
    "np.dot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "undefined-choir",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4]\n",
      " [ 5  6  7  8  9]\n",
      " [10 11 12 13 14]]\n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[120, 130, 140, 150],\n",
       "       [320, 355, 390, 425],\n",
       "       [520, 580, 640, 700]])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(15).reshape(3, 5)\n",
    "print(a)\n",
    "b = np.arange(20).reshape(5, 4)\n",
    "print(b)\n",
    "np.dot(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "italic-custody",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
